Analyzing user-preferred characteristics among merchants

ABSTRACT

A system or method is provided to analyze and determine distinguishing or secondary characteristics among merchants. In particular, the system may collect basic merchant information, such as name, type of business, products/services offered, location, owner, and other basic information regarding merchants. The system may determine merchants that are similar in type, size, location, of other factors, based on the basic information. They system may then determine distinguishing or secondary characteristics among similar merchants. The distinguishing or secondary characteristics may be determined from consumers&#39; transactions with the merchants or other data detected by sensors or devices.

CROSS REFERENCED TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/127,142, filed Sep. 10, 2018, which is a continuation of U.S. patentapplication Ser. No. 15/584,863, filed May 2, 2017, now U.S. Pat. No.10,074,119 which is a continuation of U.S. patent application Ser. No.14/666,217, filed Mar. 23, 2015, now U.S. Pat. No. 9,639,873, all ofwhich are incorporated herein by reference in their entirety.

BACKGROUND Field of the Invention

The present invention generally relates to systems and methods foranalyzing and leveraging distinguishing characteristics among merchants.

Related Art

Consumers have many choices of merchants from whom to purchase varioustypes of products or services. However, the number of choices may beoverwhelming for consumers when selecting a merchant to purchaseproducts or services from. For example, multiple gas stations may belocated in close proximity to each other. Consumers may have difficultyfiguring out the differences, if any, between the multiple gas stations,besides the posted gas prices. Thus, there is a need for a system ormethod that analyzes and determines distinguishing characteristics amongmerchants specific to user preferences.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram of a networked system suitable for analyzingand leveraging distinguishing characteristics among merchants accordingto an embodiment.

FIG. 2 is a flowchart showing a process of analyzing and determiningdistinguishing characteristics among merchants according to oneembodiment.

FIG. 3 is a flowchart showing a process for analyzing and suggestingmerchants to users according to one embodiment.

FIG. 4 is a block diagram of a computer system suitable for implementingone or more components in FIG. 1 according to one embodiment.

Embodiments of the present disclosure and their advantages are bestunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numerals are used to identifylike elements illustrated in one or more of the figures, whereinshowings therein are for purposes of illustrating embodiments of thepresent disclosure and not for purposes of limiting the same.

DETAILED DESCRIPTION

According to an embodiment, a system or method is provided to analyzeand determine distinguishing characteristics among merchants. Inparticular, the system may collect basic merchant information, such asname, type of business, products/services offered, location, owner, andother basic information regarding merchants. The system may determinemerchants that are similar in type, size, location, of other factors,based on the basic information. They system may then determinedistinguishing characteristics among similar merchants. Thedistinguishing characteristics may be determined from consumers'transactions with the merchants or other data detected by sensors ordevices. Such characteristics may be used to determine which merchantmay be recommended to a particular consumer, based in part on what theconsumer may want from the merchant, including non-primary purposes,purchases, or services.

In an embodiment, the system may determine peripheral or additionalservices or products provided by merchants as distinguishingcharacteristics. For example, three gas stations may be located at thesame intersection. The system may determine distinguishingcharacteristics based on user factors and effects on the user factorsafter users visited the gas stations. In an example, all three gasstations may offer pressurized air for car tires. However, only one ofthe three gas stations offers free pressurized air. This can bedetermined based on the purchase or transaction data of consumers whovisited the gas stations. For example, the consumers' devices may detectthat their tire pressure sensors indicate increase in tire air pressure,but the consumers are not charged for pressurized air. This indicatesthat the gas station provides free pressurized air. Other data may beused, such as postings on social networks and review sites and/orinformation from a merchant site or provided by the merchant.

In an embodiment, the system may determine included or nearby amenitiesas distinguishing characteristics. For example, one of the three gasstations may be located next to a restaurant, such that customers mayvisit the restaurant immediately before or after getting gas at the gasstation. This distinguishing characteristic may be determined based onpublic mapping data, review sites, and/or by monitoring the user'sactivities (including purchases at the restaurant) and movement. Forexample, the user's mobile device may detect that the user walks fromthe gas station to the restaurant on foot without driving. As such,having a location immediately next to a restaurant may be adistinguishing characteristic of the gas station.

In an embodiment, the system may determine a user's preferences orgoals, such as health goals or budget goals. The user's preferences orgoals may be determined based on the user's input or activities ortransactions performed by the user. For example, the user may have ahealth goal of losing weight or a budget goal of saving up for avacation. The system may recommend merchants based on the merchants'distinguishing characteristics and the user's goals or preferences. Forexample, the system may determine that the user has a health goal oflosing weight. As such, the system may recommend the gas stationfarthest from a restaurant (or at least a restaurant that sellsunhealthy food) and avoid the gas station located immediately next tothe restaurant. As such, the system recommends a gas station to preventthe user from eating (if it is not a meal time) or from eating unhealthyfood by reducing the temptation to the user based on the user's healthgoal and the distinguishing characteristics of the gas station.

In an embodiment, the system may monitor the user's behavior andtransactions to determine whether the user's behavior and transactionsare aligned with the user's goals. Further, the system may makerecommendations to better help the user follow and achieve the user'sgoal. For example, the system may determine that the user is deviatingfrom the health goal by visiting merchants selling greasy food. Thesystem may recommend travel routes or merchants that are farther awayfrom merchants that sell greasy food to help the user follow the user'shealth goal.

As such, the system determines secondary characteristics at or nearsimilarly located merchants and provides a recommendation to a user forparticular merchant based on secondary characteristics that areimportant to the user. Secondary characteristics include characteristicsthat are not the main service or type for a merchant. Using the gasstation example, gas prices is a primary characteristic, and secondarycharacteristics may include free/charged air, bathroom cleanliness,types of food, drinks, and other items offered (or not offered) at thegas station, types of merchants adjacent to the gas station,free/charged car washes, etc.

FIG. 1 is a block diagram of a networked system 100 suitable forimplementing a process for analyzing and leveraging distinguishingcharacteristics of merchants according to an embodiment. Networkedsystem 100 may comprise or implement a plurality of servers and/orsoftware components that operate to perform various payment transactionsor processes. Exemplary servers may include, for example, stand-aloneand enterprise-class servers operating a server OS such as a MICROSOFT®OS, a UNIX® OS, a LINUX® OS, or other suitable server-based OS. It canbe appreciated that the servers illustrated in FIG. 1 may be deployed inother ways and that the operations performed and/or the servicesprovided by such servers may be combined or separated for a givenimplementation and may be performed by a greater number or fewer numberof servers. One or more servers may be operated and/or maintained by thesame or different entities.

System 100 may include a user device 110, a merchant server 140, and apayment provider server 170 in communication over a network 160. Paymentprovider server 170 may be maintained by a payment service provider,such as PayPal, Inc. of San Jose, Calif. A user 105, such as a sender orconsumer, utilizes user device 110 to perform a transaction usingpayment provider server 170. User 105 may utilize user device 110 toinitiate a payment transaction, receive a transaction approval request,or reply to the request. Note that transaction, as used herein, refersto any suitable action performed using the user device, includingpayments, transfer of information, display of information, etc. Althoughonly one merchant server is shown, a plurality of merchant servers maybe utilized if the user is purchasing products or services from multiplemerchants.

User device 110, merchant server 140, and payment provider server 170may each include one or more processors, memories, and other appropriatecomponents for executing instructions such as program code and/or datastored on one or more computer readable mediums to implement the variousapplications, data, and steps described herein. For example, suchinstructions may be stored in one or more computer readable media suchas memories or data storage devices internal and/or external to variouscomponents of system 100, and/or accessible over network 160. Network160 may be implemented as a single network or a combination of multiplenetworks. For example, in various embodiments, network 160 may includethe Internet or one or more intranets, landline networks, wirelessnetworks, and/or other appropriate types of networks.

User device 110 may be implemented using any appropriate hardware andsoftware configured for wired and/or wireless communication over network160. For example, in one embodiment, user device 110 may be implementedas a personal computer (PC), a smart phone, personal digital assistant(PDA), laptop computer, and/or other types of computing devices capableof transmitting and/or receiving data, such as an iPad™ from Apple™.

User device 110 may include one or more browser applications 115 whichmay be used, for example, to provide a convenient interface to permituser 105 to browse information available over network 160. For example,in one embodiment, browser application 115 may be implemented as a webbrowser configured to view information available over the Internet, suchas a user account for setting up a shopping list and/or merchant sitesfor viewing and purchasing products and services. User device 110 mayalso include one or more toolbar applications 120 which may be used, forexample, to provide client-side processing for performing desired tasksin response to operations selected by user 105. In one embodiment,toolbar application 120 may display a user interface in connection withbrowser application 115.

User device 110 may further include other applications 125 as may bedesired in particular embodiments to provide desired features to userdevice 110. For example, other applications 125 may include securityapplications for implementing client-side security features,programmatic client applications for interfacing with appropriateapplication programming interfaces (APIs) over network 160, or othertypes of applications.

Applications 125 may also include email, texting, voice and IMapplications that allow user 105 to send and receive emails, calls, andtexts through network 160, as well as applications that enable the userto communicate, transfer information, make payments, and otherwiseutilize a smart wallet through the payment provider as discussed above.User device 110 includes one or more user identifiers 130 which may beimplemented, for example, as operating system registry entries, cookiesassociated with browser application 115, identifiers associated withhardware of user device 110, or other appropriate identifiers, such asused for payment/user/device authentication. In one embodiment, useridentifier 130 may be used by a payment service provider to associateuser 105 with a particular account maintained by the payment provider. Acommunications application 122, with associated interfaces, enables userdevice 110 to communicate within system 100.

User device 110 may include various sensors and detection devicesconfigured to detect environmental information at the user device 110.For example, the user device 110 may include a temperature sensorconfigured to detect a temperature at the user device 110, a lightsensor configured to detect ambient light, a humidity sensor configuredto detect humidity, an altitude sensor configured to detect altitude, abarometer configured to detect ambient pressure, a gyroscope and/or anaccelerometer configured to detect orientation and movement, and thelike. Thus, the user device 110 may detect various environmental changesand settings.

User device 110 also may include communication devices, such as aBluetooth device, a Near Field Communication (NFC) interface, and thelike. As such, the user device 110 may communicate with nearby devicesthat have detection devices. This allows the user device to gatheradditional detection data from other devices. For example, the userdevice 110 may communicate with the user's car console via Bluetooth toreceive car conditions detected by various sensors in the user's car.

Merchant server 140 may be maintained, for example, by a merchant orseller offering various products and/or services. The merchant may havea physical point-of-sale (POS) store front. The merchant may be aparticipating merchant who has a merchant account with the paymentservice provider. Merchant server 140 may be used for POS or onlinepurchases and transactions. Generally, merchant server 140 may bemaintained by anyone or any entity that receives money, which includescharities as well as banks and retailers. For example, a payment may bea donation to charity or a deposit to a saving account. Merchant server140 may include a database 145 identifying available products (includingdigital goods) and/or services (e.g., collectively referred to as items)which may be made available for viewing and purchase by user 105.Accordingly, merchant server 140 also may include a marketplaceapplication 150 which may be configured to serve information overnetwork 160 to browser 115 of user device 110. In one embodiment, user105 may interact with marketplace application 150 through browserapplications over network 160 in order to view various products, fooditems, or services identified in database 145.

Merchant server 140 also may include a checkout application 155 whichmay be configured to facilitate the purchase by user 105 of goods orservices online or at a physical POS or store front. Checkoutapplication 155 may be configured to accept payment information from oron behalf of user 105 through payment service provider server 170 overnetwork 160. For example, checkout application 155 may receive andprocess a payment confirmation from payment service provider server 170,as well as transmit transaction information to the payment provider andreceive information from the payment provider (e.g., a transaction ID).Checkout application 155 may be configured to receive payment via aplurality of payment methods including cash, credit cards, debit cards,checks, money orders, or the like.

Payment provider server 170 may be maintained, for example, by an onlinepayment service provider which may provide payment between user 105 andthe operator of merchant server 140. In this regard, payment providerserver 170 includes one or more payment applications 175 which may beconfigured to interact with user device 110 and/or merchant server 140over network 160 to facilitate the purchase of goods or services,communicate/display information, and send payments by user 105 of userdevice 110.

Payment provider server 170 also maintains a plurality of user accounts180, each of which may include account information 185 associated withconsumers, merchants, and funding sources, such as banks or credit cardcompanies. For example, account information 185 may include privatefinancial information of users of devices such as account numbers,passwords, device identifiers, user names, phone numbers, credit cardinformation, bank information, or other financial information which maybe used to facilitate online transactions by user 105. Advantageously,payment application 175 may be configured to interact with merchantserver 140 on behalf of user 105 during a transaction with checkoutapplication 155 to track and manage purchases made by users and whichand when funding sources are used.

User accounts 180 also may store a user's goals or preferences. Forexample, the user 105 may have a health goal or a budget goal. User'spurchase preferences may align with the user's health or budget goals.The payment provider server 170 may make merchant recommendations to theuser 105 based on the user 105's goals or preferences. Preferences orgoals may be based on user-defined or user-set preferences, userpurchase history, user search history, user calendar (upcoming birthday,anniversary, etc.), user posts, and/or posts about the user. In someembodiment, the user accounts 180 may store products or services theuser 105 is attempting to avoid. For example, the user 105 may have ahealth goal of quitting smoking. Thus, the user 105 may wish to avoidmerchants that sell cigarettes or cigars. Goals and/or preferences areused to determine what may be of interest (or disinterest) to aid indetermining whether a secondary characteristic of a merchant is more orless desirable than other similar merchants in the area.

In some embodiments, payment provider server 170 may maintain a merchantdatabase including information related to merchants. For example, themerchant database may store basic merchant information, such as name,location, ownership, product/service offered, store hours, store size,target customers, merchant categories, competitors, and the like. Themerchant database also may store unique or distinguishingcharacteristics of merchants, such as peripheral (or secondary) servicesor products offered, unique location, unique hours, nearby amenities andmerchants, and the like. The merchant database may be accessed torecommend merchants to customers. The database may be populated based onmerchant-provided information, information from publicly availablesources, such as review sites and social networks, information providedto the payment provider by users, etc.

A transaction processing application 190, which may be part of paymentapplication 175 or separate, may be configured to receive informationfrom user device 110 and/or merchant server 140 for processing andstorage in a payment database 195. Transaction processing application190 may include one or more applications to process information fromuser 105 for processing an order and payment using various selectedfunding instruments. As such, transaction processing application 190 maystore details of an order from individual users, including fundingsource used, credit options available, etc. Payment application 175 maybe further configured to determine the existence of and to manageaccounts for user 105, as well as create new accounts if necessary.

FIG. 2 is a flowchart showing a process 200 of analyzing and determiningdistinguishing characteristics among merchants according to oneembodiment. At step 202, the payment provider server 170 may collectmerchant information from merchants. In some embodiments, merchants mayset up merchant accounts at payment provider server 170 to receivepayments from customers. Merchants may provide basic merchantinformation during merchant account setup. For example, merchants mayprovide name, ownership, address, phone number, email address, webaddress, store location(s), store size(s), types of payment accepted,products/services offered, and the like. In some embodiments, thepayment provider server 170 may receive merchant information from thirdparties, such as directory services, or location services. Merchants mayalso provide information about nearby or adjacent merchants.

At step 204, the payment provider server 170 may monitor transactionsprocessed through the merchants. In particular, the payment providerserver 170 may process various transactions, such as paymenttransactions, purchase transactions, fund transfers, and othertransactions for various merchants. The transactions may include varioustransaction information such as the merchant involved in thetransaction, the customer/user involved in the transaction, time, date,location, transaction amount, items purchased (products or services),types or methods of payment, discounts applied, and the like. Thepayment provider server 170 may collect this transaction information andstore them in a transaction database for later reference or analysis.

At step 206, the payment provider server 170 may collect sensor datafrom the user device 110 and/or the merchant server 140. The user device110 may include various types of sensors that may detect and generatevarious types of sensor signals. For example, the user device 110 mayinclude an audio sensor, a vibration sensor, an ambient light sensor, alocation sensor (GPS), a Bluetooth Low Energy (BLE) indoor locationsensor, a gyroscope/accelerometer motion sensor, a temperature sensor,an altitude sensor, a humidity sensor, and other sensors for detectingenvironmental factors. Thus, the user device 110 may detect variousenvironmental factors or settings at or near merchants before, during,or after a transaction with a merchant. The sensor data may becommunicated to the payment provider server 170 to provide context forthe transaction.

In some embodiments, the user device 110 may be connected to otherdevices that have sensors. For example, the user device 110 may beconnected to a wearable device of the user 105. The wearable device mayinclude various types of sensors configured to detect the user 105'sbiometrics, such as heartbeat, body temperature, skin conductance,movement, and the like. The user device 110 may receive various sensordata form the wearable device and may communicate the sensor data to thepayment provider server 170. The biometric sensor data may be used todetermine the user 105's body condition and reactions related totransactions at a merchant.

In some embodiments, the user device 110 may be connected other devices,such as a car console installed in the user's car, entertainment consolein the user's home, laptops, computers, merchant's devices, and thelike. Sensor data generated by sensors in these other devices also maybe gathered by the user device 110 and communicated to the paymentprovider server 170. For example, the user device 110 may receive sensordata related to car conditions from the user's car console. The car mayinclude a rain sensor, an oil level sensor, a tire pressure sensor, anengine condition sensor, an engine temperature sensor, various fluidlevel sensors, various parts condition sensors, and the like. Sensordata from the car condition sensors may be used to determine any changesor repairs done to the car at a car mechanic, gas station, or other carrelated merchants.

At step 208, the payment provider server 170 may compare and contrastmerchants. In particular, the payment provider server 170 may firstorganize merchants based on their basic information. For example,merchants who offer similar products and/or services may be groupedtogether. In another example, merchants who are located near each othermay be grouped together. In still another example, merchants who targetsimilar types or demographics of customers may be grouped together. Inyet another example, merchants who have similar operation, size,franchise, and the like, may be grouped together.

As similar merchants are identified, the system may calculate similarityscores among merchants. The similarity score may indicate how similarone merchant is to another. For example, a higher similarity score mayindicate that two merchants are very similar and have many similarparameters, such as location, types of product/services, and the like.The system may focus on one or more main factors, such as types ofproduct/service offered and location, when comparing merchants by givingthese factors greater weight in the similarity score calculation. Assuch, the system may identify competing merchants who are located in thesame area and offer similar product/service. The system may select thecompeting merchants and attempt to determine distinguishingcharacteristics between them. For example, the system may identify gasstations located at the same intersection, apparel stores located in thesame mall that target similar age and/or gender of customers, autorepair shops in the same neighborhood, restaurants on the same street,and the like.

At step 210, the system may determine distinguishing characteristics ofmerchants. As noted above in step 208, the system may select similarmerchants, such as merchants that offer similar products/services,located near each other, and/or target similar customers, and mayattempt to determine distinguishing characteristics among the similarmerchants. The distinguishing characteristics may be hidden and may notbe obviously known based on the merchant's basic information. Inparticular, the system may determine these hidden and distinguishingcharacteristics based on transaction data processed at the merchants,sensor data detected at the merchants, merchant provided information,publicly available information (such as from a scrape of various websites), customer provided information (directly or indirectly throughtransactions), and the like.

The payment service provider may process transactions for the merchantsand may utilize the transaction data to infer or determinedistinguishing characteristics of merchants. In particular, the paymentprovider server 170 may compare and contrast transaction data processedat similar merchants to determine any differences or if any merchantshave a unique trait or characteristic. For example, the payment providerserver 170 may analyze transaction data from three gas stations, A, B,and C, that are located at the same intersection. The payment providerserver 170 may determine that gas stations A and B include car repaircharges on some of the customers' purchase transactions while gasstation C never has car repair charges on customers' purchasetransactions. This difference may indicate that gas station C does notoffer car repair service.

The payment service provider also may determine distinguishingcharacteristics of merchants based on sensor data detected at the userdevice 110 or other devices connected to the user device 110. Forexample, the system may analyze transaction data of gas stations A, B,and C, and note that some of the customers who visited gas stations Aand B paid for tire inflation service while customers who visited gasstation C never pay for tire inflation service. Further, the system mayanalyze sensor data from the customers' user devices and cars and notethat some of the customers who visited gas station C had their tireinflated based on the tire pressure sensor data detected by their cars'tire pressure sensors. As such, the system may determine that gasstation C offers free pressurized air for tire inflation. Thus, freetire inflation may be a distinguishing characteristic of gas station C.If customers who visited gas station C are not detected as having theirtire pressure changed, the system may determine that gas station C doesnot offer air services.

In another example, ambient noise sensors of user devices of customersmay be used to detect ambient noise at merchants. For example, theambient noise sensors of customers may pick up construction noise at ahotel. The system may determine that the hotel is under construction,which is a negative distinguishing characteristic of the hotel, albeit atemporary characteristic. When the construction noise no longer isdetected by customers' devices, the system may determine that the hotelis newly renovated, which may be a positive distinguishingcharacteristic of the hotel.

In still another example, based on the transaction data and the rainsensor data from customers' cars, the system may determine that a carrepair shop offers free car washes. In particular, the customers'transaction data do not show charges for car wash service, but the rainsensors on the customers' cars detected water drops during service atthe car shop when weather conditions at the time showed no rain ormoisture. The system may determine that the customers' cars are washedfor free during service.

In yet another example, based on the location of the merchant and thelocation where the car is parked (as detected by a location detectiondevice on the car), the system may determine whether the merchant hasconvenient parking for customers. Further, based on the movement of thecustomer's car and the movement/location of the customer at themerchant, the system may determine whether the merchant offers valetparking service, by which the merchant's personnel parks the car for thecustomer.

In some embodiments, the system may monitor transactions and movementsof customers before and/or after the customers visit a particularmerchant to determine other merchants or amenities located near oraccessible from the particular merchant. For example, some customers whovisited a gas station also visited a pizza shop immediately after thegas station, as indicated by the transaction data (purchase pizzaimmediately after purchasing gas) and the location/movement of thecustomers. The system may determine that the gas station may be locatedimmediately near a pizza shop or that the pizza shop is located withinthe gas station. The system may determine that the nearby pizza shop isa distinguishing characteristic of the gas station.

In some embodiments, the system may note that, based on the volumeand/or amount of transactions, one of the similar merchants isdisproportionally popular or unpopular compared to the other similarmerchants. The system also may detect a popular merchant based on theambient noise detected inside a merchant's location, such as a noisyrestaurant indicating popular and crowded restaurant. The system mayanalyze the transaction data, the sensor data, and other availableinformation to determine certain distinguishing characteristics thatcause a merchant to be more/less popular. For example, the system mayanalyze the movement and traffic patterns of customers who arrive andleave gas stations located at the same intersection. The system maydetermine that the popular gas station is located at a corner of anintersection that allows easier access for customers entering andexiting the gas station.

In another example, the system may analyze the biometric sensor data ofcustomers who visited similar restaurants and may determine that morecustomers who visited a popular restaurant have biometric signatures ofbeing in a pleasant state. The biometric signatures may includeheartbeat, body temperature, skin conductance, and the like as detectedby biometric sensors included in a wearable device of a customer. Thismay indicate that the restaurant offers good service and/or good food,including highly rated or popular food of a certain type, such as aparticular restaurant having a really good hot dog or pizza. The systemmay also analyze communication from customers regarding the restaurant,such as pictures, comments, reviews, and other communication postedonline, such as on social media networks, review sites, and the like.The system may determine distinguishing characteristics of therestaurant based on various information.

Thus, the process 200 may allow the system to collect information aboutthe merchants from various sources including basic information providedby the merchants and/or customers (both directly and indirectly),transaction data, sensor data, location information, customercommunication and the like, and may determine distinguishingcharacteristics of merchants. In particular, distinguishingcharacteristics among similar merchants may be determined to helpcustomers select merchants. The distinguishing characteristics may bepermanent or temporary which changes over time, such as seasonal orcertain days of the week. The system may update the distinguishingcharacteristics over time to reflect the most up to date information.

FIG. 3 is a flowchart showing a process 300 for analyzing and suggestingmerchants to users according to one embodiment. At step 302, the systemmay monitor user activities and transactions. The system may monitoruser activities via user device 110. As noted above, the user device 110may include sensors to detect user activities, such as location,movement, travel routes, transactions, biometric information,environmental factors, and the like. In particular, the system maycollect user activities related to merchants, such as transactions madewith merchants, visits at merchants' locations, sensor data detected ator near merchant's locations, and sensor data changes made at merchant'slocations. The collected user activities may be analyzed to determinethe user 105's activities at merchants, such as what transactions tookplace, what products/services received, and other activities. Forexample, when the user 105's car is serviced at a repair shop, thevarious sensors in the user 105's car may detect changes to the carcondition. Sensor data from the user 105's car may be communicated fromthe car console to the system. Thus, the system may determine whatservices were performed on the car based on the sensor data. The systemalso may determine what services were paid for at the repair shop basedon the user 105's transaction data at the repair shop.

At step 304, the system may determine user preferences or goals. In anembodiment, the user 105 may provide or input user 105's goals ofpreferences. For example, the user 105 may have a health goal, such as atarget weight loss, training for a marathon, or the like. In anotherexample, the user 105 may have a financial goal, such as saving acertain amount of money. In still another example, the user 105 may havea work or school related goal for certain achievements or targets. Insome embodiments, the system may determine the user 105's goals based onthe user 105's activities, as noted in step 302.

In some embodiments, the system may determine the user 105's currentpreferences based on the time, location, and environment. For example,if it is meal time, a preference may be a restaurant or a specific typeof restaurant based on the user's food preferences. In another example,if the user's car is detected as being dirty or the user has a date thenext night, a preference may be a car wash. Thus, the system may analyzethe environmental conditions, user 105's to-do list, schedule, calendar,time, location, season, and the like to the user 105's currentpreference. For example, if there are three nearby gas stations, and theuser 105 needs gas and all three gas stations are about the same price,secondary factors based on user preferences may be used to suggest oneof the gas stations to the user. In particular, the importance of thesecondary factor is based on what the user 105 may currently want(eating healthy, buying a gift, getting air in tire, getting a car wash,a clean restroom (maybe a user who has been on the road for a longtime), buying cigarettes, etc.

In some embodiments, the system may help the user 105 follow a long termgoal when the user 105's current preference does not align with the user105's long term goal. For example, the user 105 may have a currentpreference of buying fast food when the user 105 is near a food court,but the user 105 may have a long term goal of no-fast food diet. Thus,the system may help the user 105 avoid buying fast food by directing theuser 105 away from the food court or by recommending alternative healthyfood places to the user 105.

At step 306, the system may determine user activities and/ortransactions deviating from preferences or goals. In particular, thesystem may analyze user activities that do not match the user 105'sgoals or preferences. For example, the system may determine that theuser 105 visited a pizza shop and purchased a slice of pizza. However,the user 105 currently has a no-pizza diet plan. As such, the system maydetermine that the user 105 is deviating or violating the user 105'sgoal or preference.

In some embodiments, the system may attempt to determine a reason forthe user 105's deviation from the goals or preferences. For example, theuser 105 may have purchased the pizza because the user 105 was gettinggas at a nearby gas station and was enticed by the pizza shop locatednext to the gas station. Thus, the system may analyze transaction data,sensor data, and other user activities of the user 105 to determine thecause or reason for the deviation or violation.

In some embodiments, the system may learn or determine the user 105'stendencies and self-control with respect to different items the user 105should avoid based on the goals or preferences. The system may calculatea trust score relating to these items that the user 105 should avoid. Ahigher trust score for an item means that the user 105 has no problemwith the item and has high probability of success in resisting the item.On the other hand, a lower trust score for an item may indicate that theuser 105 has low probability of success in resisting the item. The trustscore may be calculated based on the user 105 activities in view of apurchase opportunity presented to the user 105. For example, the systemmay determine that the user 105 has been presented many opportunities topurchase a prohibited item, but the user 105 did not purchased theprohibited item. Thus, the system may calculate a higher trust score forthe user 105 with respect to the prohibited item. In an embodiment, thetrust score may be determined based on the user 105's transaction data,such as the frequency of prohibited items purchased, the last timeprohibited items were purchased, and the like.

At step 308, the system may recommend merchants based on userpreferences or goals and distinguishing characteristics of merchants.Based on the reasons or causes of the user 105's deviation or violationof the user 105's goals or preferences, the system may recommendmerchants, routes, and/or schedules to help the user 105 keep the user105's goals or preferences. Further, the recommendations may also bemade in view of the distinguishing characteristics of merchants. Forexample, the system may determine that the user 105 is easily enticed topurchase a pizza if the user 105 is near a pizza shop based on the trustscores. Thus, the system may avoid merchants that are near pizza shops.Further, the system may recommend travel routes and/or schedules thatavoid pizza shops.

In an embodiment, the system may include health goal service which isconfigured to analyze and determine health goals of the user. As such,the user 105's health goals, health rules, and/or preferences may betaken into consideration along with distinguishing characteristics ofmerchants for merchant recommendations. For example, the user may be ona particular type of diet, such as a low fat diet, or the user may beallergic to gluten or wheat. The system may digitally harvest menus fromfood merchants and/or customer transaction data to determine merchantsthat should be avoided by the user. The health goal service may filterout merchants that should be avoided by the user based on the user'sgoals or preferences. If the health goal service clears a merchant, thenthe system may recommend that merchant to the user.

The health goal service may assign scores to potential merchants basedon the health goals of the user in view of the distinguishingcharacteristics of the merchants. For example, a pizza shop that offersgood salads may receive a higher score than pizza shops that do notoffer healthy alternatives. This allows users to make judgment calls insituations where there is not best choice, but only the least bad choicewhen pizza shops are the only available food options. The health goalservice and the system may monitor user behavior when a place that isnot recommended is visited by the user, such as a pizza shop with saladswhen the user is on a diet. If the user's transaction data indicatesthat the user ends up purchasing items or services that are restrictedby the health goal service, the system may determine that the user isviolating the health goals. As such, the system and the health goalservice may become stricter for successive recommendations (avoiding allplaces that offer restricted items), because the user is not able toresist the restricted items when they are presented to them.

In an embodiment, the health goal service also may data mine caloriecounts from merchants' food menus, such as via public API service calls.For example, restaurants, food chains, and other food merchants may postcalorie counts of the items they offer online. The system and/or thehealth goal service may compare calorie counts of different food itemsto determine the relative health benefit of the food items. For example,if a salad actually has more calories than a slice of pizza, then theuser's choice to purchase a slice of pizza instead of a salad may notcount against the user. In some embodiments, the health goal service maytake into account the portion and size of food items purchased by theuser. Portion and size information may be determined from user'stransaction data, such as large or small fries. Thus, the health goalservice may consider portion and size as factors in makingrecommendations for the user. Further, portion control based ontransaction data or transaction history may be used to determine if theuser has met his or her health goal. If the user has met the healthgoal, the event may be fed back into the system for the user for futurerecommendations. Thus, the system may learn the user's habits andtendencies over time. In some embodiments, the system may make similarrecommendations for similar users or users who have similar healthgoals. As such, the system may crowd source health goal recommendationsand results from the crowd to make recommendations for a particularuser.

Thus, a service provider may provide one or more recommended merchantsto a user that satisfies both a primary purpose of the user visiting themerchant and secondary reasons (based on preferences and/or goals, whichcan be based on habit, history, user-defined information, currentconditions, etc.). For example, if multiple merchants offer the sameprimary service/products and are located near each other (or along aroute of the user), the user may not know which one to visit, especiallyif the primary service/products have similar pricing. In that case,secondary factors/considerations of the merchants may be taken intoaccount so that the user is recommended one or more merchants offering asecondary factor/consideration that is desirable to the user, whichallows a system to provide an informed recommendation that is beneficialto the user.

The above processes 200 and 300 may be executed by user device 110. Insome embodiments, the processes 200 and 300 may be executed at merchantdevice 140 or payment provider server 170. In some other embodiments,above processes 200 and 300 may be executed by one or more of userdevice 110, merchant device 140, and payment provider server 170 incoordination with each other. Note that the various steps and processesdescribed herein may be omitted, combined, and/or performed in adifferent sequence as desired.

FIG. 4 is a block diagram of a computer system 400 suitable forimplementing one or more embodiments of the present disclosure. Invarious implementations, the user device may comprise a personalcomputing device (e.g., smart phone, a computing tablet, a personalcomputer, laptop, PDA, Bluetooth device, key FOB, badge, etc.) capableof communicating with the network. The merchant and/or payment providermay utilize a network computing device (e.g., a network server) capableof communicating with the network. It should be appreciated that each ofthe devices utilized by users, merchants, and payment providers may beimplemented as computer system 400 in a manner as follows.

Computer system 400 includes a bus 402 or other communication mechanismfor communicating information data, signals, and information betweenvarious components of computer system 400. Components include aninput/output (I/O) component 404 that processes a user action, such asselecting keys from a keypad/keyboard, selecting one or more buttons orlinks, etc., and sends a corresponding signal to bus 402. I/O component404 may also include an output component, such as a display 411 and acursor control 413 (such as a keyboard, keypad, mouse, etc.). Anoptional audio input/output component 405 may also be included to allowa user to use voice for inputting information by converting audiosignals. Audio I/O component 405 may allow the user to hear audio. Atransceiver or network interface 406 transmits and receives signalsbetween computer system 400 and other devices, such as another userdevice, a merchant server, or a payment provider server via network 160.In one embodiment, the transmission is wireless, although othertransmission mediums and methods may also be suitable. A processor 412,which can be a micro-controller, digital signal processor (DSP), orother processing component, processes these various signals, such as fordisplay on computer system 400 or transmission to other devices via acommunication link 418. Processor 412 may also control transmission ofinformation, such as cookies or IP addresses, to other devices.

Components of computer system 400 also include a system memory component414 (e.g., RAM), a static storage component 416 (e.g., ROM), and/or adisk drive 417. Computer system 400 performs specific operations byprocessor 412 and other components by executing one or more sequences ofinstructions contained in system memory component 414. Logic may beencoded in a computer readable medium, which may refer to any mediumthat participates in providing instructions to processor 412 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media. Invarious implementations, non-volatile media includes optical or magneticdisks, volatile media includes dynamic memory, such as system memorycomponent 414, and transmission media includes coaxial cables, copperwire, and fiber optics, including wires that comprise bus 402. In oneembodiment, the logic is encoded in non-transitory computer readablemedium. In one example, transmission media may take the form of acousticor light waves, such as those generated during radio wave, optical, andinfrared data communications.

Some common forms of computer readable media includes, for example,floppy disk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EEPROM,FLASH-EEPROM, any other memory chip or cartridge, or any other mediumfrom which a computer is adapted to read.

In various embodiments of the present disclosure, execution ofinstruction sequences to practice the present disclosure may beperformed by computer system 400. In various other embodiments of thepresent disclosure, a plurality of computer systems 400 coupled bycommunication link 418 to the network (e.g., such as a LAN, WLAN, PTSN,and/or various other wired or wireless networks, includingtelecommunications, mobile, and cellular phone networks) may performinstruction sequences to practice the present disclosure in coordinationwith one another.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also, where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from the scope of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components andvice-versa.

Software, in accordance with the present disclosure, such as programcode and/or data, may be stored on one or more computer readablemediums. It is also contemplated that software identified herein may beimplemented using one or more general purpose or specific purposecomputers and/or computer systems, networked and/or otherwise. Whereapplicable, the ordering of various steps described herein may bechanged, combined into composite steps, and/or separated into sub-stepsto provide features described herein.

The foregoing disclosure is not intended to limit the present disclosureto the precise forms or particular fields of use disclosed. As such, itis contemplated that various alternate embodiments and/or modificationsto the present disclosure, whether explicitly described or impliedherein, are possible in light of the disclosure. Having thus describedembodiments of the present disclosure, persons of ordinary skill in theart will recognize that changes may be made in form and detail withoutdeparting from the scope of the present disclosure. Thus, the presentdisclosure is limited only by the claims.

1. (canceled)
 2. A system comprising: a non-transitory memory storingmerchant information; and one or more hardware processors coupled to thenon-transitory memory and configured to read instructions from thenon-transitory memory to cause the system to perform operationscomprising: determining a location of a user; determining a plurality ofmerchants offering similar services or products near the location of theuser; receiving sensor data detected by user devices of users visitingthe plurality of merchants, wherein the sensor data comprises biometricdata detected from the user via a wearable device of the user, andwherein the biometric data comprises one or more of a body temperature,a heart rate, a skin conductance, and a body movement; determiningsecondary characteristics of the plurality of merchants based on thereceived sensor data; determining one or more preferences of the user;and communicating a recommendation of a subset of the plurality ofmerchants to the user based on the one or more preferences of the userand the secondary characteristics of the plurality of merchants.
 3. Thesystem of claim 2, wherein the secondary characteristics are secondaryservices or products offered by one or more of the plurality ofmerchants.
 4. The system of claim 2, wherein the secondarycharacteristics are other merchants located near one or more of theplurality of merchants.
 5. The system of claim 2, wherein the secondarycharacteristics comprise secondary services or products offered by oneor more of the plurality of merchants.
 6. The system of claim 2, whereinthe one or more hardware processors are further configured to cause thesystem to perform operations comprising collecting transaction dataprocessed at the plurality of merchants, and wherein the secondarycharacteristics of the plurality of merchants are determined furtherbased on the transaction data.
 7. The system of claim 2, wherein theplurality of merchants are similar in one or more of location,product/service offered, type, size, and type of customers.
 8. Thesystem of claim 2, wherein the sensor data further comprises one or moreof environmental data detected at the location of the merchant, anddevice conditions detected by sensors at another device of the user. 9.The system of claim 8, wherein the environmental data comprises one ormore of a temperature, an atmosphere pressure, a humidity, an altitude,an ambient light level, and an ambient noise level.
 10. The system ofclaim 8, wherein the another device is a transportation vehicle and thedevice conditions comprise one or more of a tire pressure, a rainsensor, an engine condition, a gasoline level, and fluid levels.
 11. Amethod comprising: determining, by one or more processors, a location ofa user; determining, by the one or more processors, a plurality ofmerchants offering similar services or products near the location of theuser; receiving, by one or more processors, sensor data detected by userdevices of users visiting the plurality of merchants, wherein the sensordata comprises biometric data detected from the user via a wearabledevice of the user, and wherein the biometric data comprises one or moreof a body temperature, a heart rate, a skin conductance, and a bodymovement; determining, by the one or more processors, secondarycharacteristics of the plurality of merchants on the received sensordata; determining, by the one or more processors, one or morepreferences of the user; and communicating, by the one or moreprocessors, a recommendation of a subset of the plurality of merchantsto the user based on the one or more preferences of the user and thesecondary characteristics of the plurality of merchants.
 12. The methodof claim 11, further comprising collecting transaction data processed atthe plurality of merchants, and wherein the secondary characteristics ofthe plurality of merchants are determined further based on thetransaction data.
 13. The method of claim 11, wherein the one or morepreferences of the user comprise one or more long term goals.
 14. Themethod of claim 13, wherein the one or more long term goals comprise oneor more of a health goal and a financial goal.
 15. The method of claim13, further comprising: monitoring user transactions conducted at theplurality of merchants; determining user transactions that are deviatingfrom the one or more long term goals of the user; and communicating, toa device of the user for display, recommendations of merchants thatre-align user transactions with the long term goals of the user.
 16. Themethod of claim 13, wherein the one or more preferences of the userfurther comprise one or more current preferences.
 17. The method ofclaim 16, further comprising recommending merchants based on the one ormore current preferences of the user.
 18. The method of claim 17,further comprising communicating, to a device of the user for display,recommendations of merchants that avoid the one or more currentpreferences of the user when the one or more current preferences of theuser are contrary to the one or more long term goals of the user.
 19. Anon-transitory machine-readable medium comprising a plurality ofmachine-readable instructions which when executed by one or moreprocessors are adapted to cause the one or more processors to perform amethod comprising: determining a location of a user; determining aplurality of merchants offering similar services or products near thelocation of the user; receiving sensor data detected by user devices ofusers visiting the plurality of merchants, wherein the sensor datacomprises biometric data detected from the user via a wearable device ofthe user, and wherein the biometric data comprises one or more of a bodytemperature, a heart rate, a skin conductance, and a body movement;determining secondary characteristics of the plurality of merchantsbased on the received sensor data; determining one or more preferencesof the user; and communicating a recommendation of a subset of theplurality of merchants to the user based on the one or more preferencesof the user and the secondary characteristics of the plurality ofmerchants.
 20. The non-transitory machine-readable medium of claim 19,wherein the method further comprises collecting transaction dataprocessed at the plurality of merchants, and wherein the secondarycharacteristics of the plurality of merchants are determined furtherbased on the transaction data.
 21. The non-transitory machine-readablemedium of claim 19, wherein the sensor data further comprises one ormore of environmental data detected at the location of the merchant, anddevice conditions detected by sensors at another device of the user, andwherein the environmental data comprises one or more of a temperature,an atmosphere pressure, a humidity, an altitude, an ambient light level,and an ambient noise level.